Gender detection and identifying one's handwriting with handwriting analysis

Topaloğlu, Murat; Ekmekci, Seher

doi:10.1016/j.eswa.2017.03.001

Cited by 33 publications

(19 citation statements)

References 9 publications

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“…Topaloglu and Ekmekci [10] proposed gender detection for handwriting analysis. The method extracts attributes of handwritten characters for identifying gender, male or female.…”

Section: Related Workmentioning

confidence: 99%

Graphology based handwritten character analysis for human behaviour identification

Ghosh

Shivakumara

Roy

et al. 2020

CAAI trans. intell. technol.

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Graphology‐based handwriting analysis to identify human behavior, irrespective of applications, is interesting. Unlike existing methods that use characters, words and sentences for behavioural analysis with human intervention, we propose an automatic method by analysing a few handwritten English lowercase characters from a to z to identify person behaviours. The proposed method extracts structural features, such as loops, slants, cursive, straight lines, stroke thickness, contour shapes, aspect ratio and other geometrical properties, from different zones of isolated character images to derive the hypothesis based on a dictionary of Graphological rules. The derived hypothesis has the ability to categorise the personal, positive, and negative social aspects of an individual. To evaluate the proposed method, an automatic system is developed which accepts characters from a to z written by different individuals across different genders and age groups. This automatic privacy projected system is available on the website (http://subha.pythonanywhere.com). For quantitative evaluation of the proposed method, several people are requested to use the system to check their characteristics with the system automatic response based on his/her handwriting by choosing to agree or disagree options. The automatic system receives 5300 responses from the users, for which, the proposed method achieves 86.70% accuracy.

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“…Topaloglu and Ekmekci [10] proposed gender detection for handwriting analysis. The method extracts attributes of handwritten characters for identifying gender, male or female.…”

Section: Related Workmentioning

confidence: 99%

Graphology based handwritten character analysis for human behaviour identification

Ghosh

Shivakumara

Roy

et al. 2020

CAAI trans. intell. technol.

View full text Add to dashboard Cite

show abstract

“…Many researchers have worked on writer identification of handwritten signatures and handwritings (Al-ma'adeed and Hassaïne, 2014;Ibrahim et al, 2014;Siddiqi et al, 2014;SRIVASTAVA, 2014;Mirza et al, 2016;Bouadjenek et al, 2017;Topaloglu and Ekmekci, 2017;Navya et al, 2018;Baboria et al, 2019;Faundez-Zanuy et al, 2020). However, it is observed that very less effort is done on gender identification based on offline handwritten signatures.…”

Section: Related Workmentioning

confidence: 99%

Behavioral Biometric Data Analysis for Gender Classification Using Feature Fusion and Machine Learning

et al. 2021

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Biometric security applications have been employed for providing a higher security in several access control systems during the past few years. The handwritten signature is the most widely accepted behavioral biometric trait for authenticating the documents like letters, contracts, wills, MOU’s, etc. for validation in day to day life. In this paper, a novel algorithm to detect gender of individuals based on the image of their handwritten signatures is proposed. The proposed work is based on the fusion of textural and statistical features extracted from the signature images. The LBP and HOG features represent the texture. The writer’s gender classification is carried out using machine learning techniques. The proposed technique is evaluated on own dataset of 4,790 signatures and realized an encouraging accuracy of 96.17, 98.72 and 100% for k-NN, decision tree and Support Vector Machine classifiers, respectively. The proposed method is expected to be useful in design of efficient computer vision tools for authentication and forensic investigation of documents with handwritten signatures.

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“…Alsmearat et al [14] presented two techniques of word boxes and word morphology to select the gender of Arabic texts author in cyberspace. In addition, Topaloglu and Ekmekci [15] proposed a user gender detection model based on personal handwriting in graphology science. The decision tree was considered as the applied algorithm for evaluating the proposed model.…”

Section: Related Workmentioning

confidence: 99%

An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network

et al. 2020

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Author gender detection (AGD) is a serious and crucial issue in Internet security applications, in particular in email, messenger, and social network communications. Detecting the gender of communication partner helps preventing massive fraud and abuses happening through social media such as email, blogs, forums. Text and writings of people on the Internet have valuable information that can be used to identify the gender of an author. Machine learning and meta-heuristic algorithms are valuable techniques to extract hidden patterns useful for detecting gender of a text. In this paper, an artificial neural network (ANN) is employed as a classifier to detect the gender of an email author and the whale optimization algorithm (WOA) is used to find optimal weights and biases for improving the accuracy of the ANN classification. Through this combination of ANN and WOA an accuracy of 98%, precision of 97.16%, and recall of 99.67% were achieved, which indicates the superiority of the proposed method on Bayesian networks, regression, decision tree, support vector machine, and ANN examined. INDEX TERMS Author gender detection, machine learning, artificial neural network, whale optimization algorithm.

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Gender detection and identifying one's handwriting with handwriting analysis

Cited by 33 publications

References 9 publications

Graphology based handwritten character analysis for human behaviour identification

Graphology based handwritten character analysis for human behaviour identification

Behavioral Biometric Data Analysis for Gender Classification Using Feature Fusion and Machine Learning

An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network

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